A novel algorithm for color constancy

Abstract

Color constancy is the skill by which it is possible to tell the color of an object even under a colored light. I interpret the color of an object as its color under a fixed canonical light, rather than as a surface reflectance function. This leads to an analysis that shows two distinct sets of circumstances under which color constancy is possible. In this framework, color constancy requires estimating the illuminant under which the image was taken. The estimate is then used to choose one of a set of linear maps, which is applied to the image to yield a color descriptor at each point. This set of maps is computed in advance.The illuminant can be estimated using image measurements alone, because, given a number of weak assumptions detailed in the text, the color of the illuminant is constrained by the colors observed in the image. This constraint arises from the fact that surfaces can reflect no more light than is cast on them. For example, if one observes a patch that excites the red receptor strongly, the illuminant cannot have been deep blue.Two algorithms are possible using this constraint, corresponding to different assumptions about the world. The first algorithm, Crule will work for any surface reflectance. Crule corresponds to a form of coefficient rule, but obtains the coefficients by using constraints on illuminant color. The set of illuminants for which Crule will be successful depends strongly on the choice of photoreceptors: for narrowband photoreceptors, Crule will work in an unrestricted world. The second algorithm, Mwext, requires that both surface reflectances and illuminants be chosen from finite dimensional spaces; but under these restrictive conditions it can recover a large number of parameters in the illuminant, and is not an attractive model of human color constancy.Crule has been tested on real images of Mondriaans, and works well. I show results for Crule and for the Retinex algorithm of Land (Land 1971;Land 1983;Land 1985) operating on a number of real images. The experimental work shows that for good constancy, a color constancy system will need to adjust the gain of the receptors it employs in a fashion analagous to adaptation in humans.

Color space analysis of mutual illumination

“…This idea has been explored for various purposes over a period of time and resulted in the development of many algorithms of kNN classifier design using reduced sample sets. Particularly noteworthy are the condensed NN (CNN) [5], the reduced NN (RNN) [6], and the edited NN (ENN) [7]. In these algorithms, iterative processes are used to test the effect on the classification performance as each individual point is moved in and out of the design set, and only the "good" points are retained.…”

Integrated Saturation Weighting based Color Cat Algorithm

“…To put it differently, histogram equalization best parts the is bordered by and perimeters between various objects, but may reduce the local information on these physical objects, and not adequate regarding local development. Another consequence just for this mergence will be the production regarding over development and saturation artifacts [10].…”

Color Models of Shadow Detection in Video Scenes

“…(1) is already limited by several scene assumptions (Forsyth, 1990), in general, it is still too difficult to exploit appropriate information about the corresponding background-shadow values, since the components of the illumination function are unknown. With further strong simplifications (Forsyth, 1990) eq. (1) implies the well-known 'constant ratio' rule.…”

“…In the CCD camera model (Forsyth, 1990) the RGB sensors are narrow banded and the constant ratio rule is valid for each color channel independently. Accordingly, the shadow descriptor is a triple…”